Changing Minds: Exploring Brain-Computer Interface Experiences with High School Students

Relatively little research exists on the use of experiences with EEG devices to support brain-computer interface (BCI) education. In this paper, we draw on techniques from BCI, visual programming languages, and computer science education to design a web-based environment for BCI education. We conducted a study with 14 10th and 11th grade high school students to investigate the effects of EEG experiences on students' BCI self-efficacy. We also explored the usability of a hybrid block-flow based visual interface for students new to BCI. Our results suggest that experiences with EEG devices may increase high school students' BCI self-efficacy. Furthermore, our findings offer insights for engaging high school students in BCI.

[1]  Sayamindu Dasgupta,et al.  How “Wide Walls” Can Increase Engagement: Evidence From a Natural Experiment in Scratch , 2018, CHI.

[2]  G. Pfurtscheller,et al.  EEG-based neuroprosthesis control: A step towards clinical practice , 2005, Neuroscience Letters.

[3]  Jérémy Frey,et al.  Teegi: tangible EEG interface , 2014, UIST.

[4]  Michael Goesele,et al.  How Human Am I?: EEG-based Evaluation of Virtual Characters , 2017, CHI.

[5]  Alissa Nicole Antle,et al.  Using neurofeedback to teach self-regulation to children living in poverty , 2015, IDC.

[6]  R. T. Pivik,et al.  Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. , 1993, Psychophysiology.

[7]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[8]  Chin-Teng Lin,et al.  Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials , 2019, CHI.

[9]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[10]  Thorsten O. Zander,et al.  Detecting affective covert user states with passive brain-computer interfaces , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[11]  Jérémy Frey,et al.  Framework for Electroencephalography-based Evaluation of User Experience , 2016, CHI.

[12]  Steve Benford,et al.  From Director's Cut to User's Cut: to Watch a Brain-Controlled Film is to Edit it , 2019, CHI.

[13]  Majed Marji Learn to Program with Scratch: A Visual Introduction to Programming with Games, Art, Science, and Math , 2014 .

[14]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[15]  José del R. Millán,et al.  Brain-Controlled Wheelchairs: A Robotic Architecture , 2013, IEEE Robotics & Automation Magazine.

[16]  Michael Rohs,et al.  Emotion Actuator: Embodied Emotional Feedback through Electroencephalography and Electrical Muscle Stimulation , 2017, CHI.

[17]  Guillaume Chanel,et al.  Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  Fabio Zambetta,et al.  Towards Understanding the Design of Positive Pre-sleep Through a Neurofeedback Artistic Experience , 2019, CHI.

[19]  Jérémy Frey,et al.  TOBE: Tangible Out-of-Body Experience , 2015, TEI.

[20]  Naomi Johnson,et al.  Evaluating the Impact of a Mobile Neurofeedback App for Young Children at School and Home , 2019, CHI.

[21]  Robert J. K. Jacob,et al.  Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines , 2009, UIST '09.

[22]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[23]  Roel Vertegaal,et al.  Using mental load for managing interruptions in physiologically attentive user interfaces , 2004, CHI EA '04.

[24]  Niels Henze,et al.  EngageMeter: A System for Implicit Audience Engagement Sensing Using Electroencephalography , 2017, CHI.

[25]  Edward Cutrell,et al.  BCI for passive input in HCI , 2007 .

[26]  Juan E. Gilbert,et al.  Let's learn!: enhancing user's engagement levels through passive brain-computer interfaces , 2013, CHI Extended Abstracts.

[27]  Jie Liu,et al.  FOCUS: enhancing children's engagement in reading by using contextual BCI training sessions , 2014, CHI.

[28]  Desney S. Tan,et al.  Feasibility and pragmatics of classifying working memory load with an electroencephalograph , 2008, CHI.

[29]  Bilge Mutlu,et al.  ARTFul: adaptive review technology for flipped learning , 2013, CHI.

[30]  James T. Miller,et al.  An Empirical Evaluation of the System Usability Scale , 2008, Int. J. Hum. Comput. Interact..

[31]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[32]  Juan E. Gilbert,et al.  Brains and Blocks , 2019, ACM Trans. Comput. Educ..

[33]  Jeffrey G. Gray,et al.  WebBCI: An Electroencephalography Toolkit Built on Modern Web Technologies , 2018, HCI.

[34]  John Maloney,et al.  The Scratch Programming Language and Environment , 2010, TOCE.

[35]  Regan L. Mandryk,et al.  Using Positive or Negative Reinforcement in Neurofeedback Games for Training Self-Regulation , 2016, CHI PLAY.

[36]  Jack Mostow,et al.  Toward unobtrusive measurement of reading comprehension using low-cost EEG , 2014, LAK '14.

[37]  Bilge Mutlu,et al.  Pay attention!: designing adaptive agents that monitor and improve user engagement , 2012, CHI.

[38]  Jung-Tai King,et al.  An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance , 2016, CHI.